Systems and Methods for Generating a Home Score and Modifications for a User

ABSTRACT

Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving at least one of home data for a property or user data for a user; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; (3) receiving, from the user, a home modification indication; (4) modifying, based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and (5) generating, based upon the one or more modified home score factors, a home score for the property.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 17/816,391, entitled “SYSTEMS AND METHODS FORGENERATING A HOME SCORE AND MODIFICATIONS FOR A USER,” filed Jul. 29,2022, which claims priority to and the benefit of the filing date ofprovisional U.S. Patent Application No. 63/332,972 entitled “SYSTEMS ANDMETHODS FOR GENERATING A HOME SCORE AND MODIFICATIONS FOR A USER,” filedon Apr. 20, 2022 and provisional U.S. Patent Application No. 63/333,519entitled “SYSTEMS AND METHODS FOR GENERATING A HOME SCORE ANDMODIFICATIONS FOR A USER,” filed on Apr. 21, 2022. The entire contentsof each of the preceding applications are hereby expressly incorporatedherein by reference.

FIELD OF THE DISCLOSURE

Systems and methods are disclosed for evaluating and generating amodified home score, and providing recommendations for a property usinghome data.

BACKGROUND

When moving to a new home, a user may not be aware of importantinformation regarding the property, the surrounding area, and/oravailability of important public services. This may be particularly truefor when a user is moving between states, countries, or areas withdifferences between environments. Moreover, current methods of providinginformation to potential homeowners and/or individuals moving betweensuch locations may be inefficient or may not provide such importantdetails.

Further, when performing maintenance or updates to a home, a user maynot be aware of how important various maintenance tasks and/or updatesmay be to the well-being and integrity of the home. Moreover,conventional methods of providing such information to homeowners areoften inefficient, and generally lack security and privacy. Similarly,conventional methods for providing such information may lack importantdetails that a homeowner would use to make an informed decision.Conventional techniques may have other drawbacks as well.

SUMMARY

The present embodiments may relate to, inter alia, acomputer-implemented method for efficiently evaluating and generating ametric for a property that is representative of (i) important featuresassociated with a property, and/or (ii) important differences between apast or present property and a new property to which a user is moving.The present embodiments may also relate to a computer-implemented methodfor efficiently, securely, and privately evaluating and modifying ametric for a property that is representative of modifications and/orupdates to the property or features associated with the property.

In one aspect, a computer-implemented method for evaluating andgamifying maintenance for a property by a user may be provided. Themethod may be implemented via one or more local or remote processors,servers, sensors, transceivers, memory units, and/or other electronic orelectrical components. The method may include: (1) retrieving, by one ormore processors, at least one of home data for a property or user datafor a user; (2) determining, by the one or more processors and using atrained machine learning evaluation model, one or more home scorefactors based upon at least one of the home data or the user data; (3)receiving, by the one or more processors and from the user, a homemodification indication; (4) modifying, by the one or more processorsand based upon the home modification indication, at least one of the oneor more home score factors to create one or more modified home scorefactors; and/or (5) generating, by the one or more processors and basedupon the one or more modified home score factors, a home score for theproperty. The method may include additional, less, or alternativeactions, including those discussed elsewhere herein.

For instance, in some embodiments, the computer-implemented method mayinclude determining, based upon the user data, previous homecharacteristic data. The one or more home score factors may include oneor more difference factors different between the previous homecharacteristic data and the home data. In further embodiments, thecomputer-implemented method may further include generating one or morerecommended actions for the user to perform based upon the one or moredifference factors. The one or more recommended actions may be homemodification indications.

In some embodiments, at least some of the home data is retrieved fromone or more smart devices on the property and the home data may includeat least one of: location data, environment data, first responder data,home structure data, and adherence to local construction codes.

In certain embodiments, determining the one or more home score factorsmay include weighting the home data and the user data, and thecomputer-implemented method may include (i) determining influential homecharacteristic factors, wherein the influential home characteristicfactors are a subset of the home characteristic data with the highestweight; and (ii) displaying the influential home characteristic factorsto the user, such as on a user mobile device.

In some embodiments, the home modification indication may include atleast one of: completion of a home maintenance learning module,performance of maintenance on a component of the property, an averagepower consumption for the property, an average water consumption for theproperty, and an indication of average occupancy. Further, generatingthe home score may include anonymizing the home score such thatanonymized underwriting can be performed using the anonymized homescore.

In another aspect, a computing device for evaluating and gamifyingmaintenance for a property by a user may be provided. The computingdevice may include: one or more processors; a communication unit; and anon-transitory computer-readable medium coupled to the one or moreprocessors and the communication unit and storing instructions thereonthat, when executed by the one or more processors, cause the computingdevice to: (1) retrieve home data for a property and user data for auser; (2) determine, using a trained machine learning evaluation model,one or more home score factors based upon at least one of the home dataor the user data; (3) receive, from the user, a home modificationindication; (4) modify, based upon the home modification indication, atleast one of the one or more home score factors to create one or moremodified home score factors; and/or (5) generate, based upon the one ormore modified home score factors, a home score for the property. Thecomputing device may include additional, less, or alternatefunctionality, including that discussed elsewhere herein.

For instance, in some embodiments, the non-transitory computer-readablemedium further stores instructions that, when executed by the one ormore processors, cause the computing device to determine, based upon theuser data, previous home characteristic data. The one or more home scorefactors may include one or more difference factors different between theprevious home characteristic data and the home data. In furtherembodiments, the non-transitory computer-readable medium further storesinstructions that, when executed by the one or more processors, causethe computing device to generate one or more recommended actions for theuser to perform based upon the one or more difference factors. The oneor more recommended actions may be home modification indications.

In some embodiments, at least some of the home data is retrieved fromone or more smart devices on the property and the home data may includeat least one of: location data, environment data, first responder data,home structure data, and adherence to local construction codes.

In certain embodiments, determining the one or more home score factorsmay include weighting the home data and the user data, and thenon-transitory computer-readable medium further stores instructionsthat, when executed by the one or more processors, cause the computingdevice to (i) determine influential home characteristic factors, wherethe influential home characteristic factors may be a subset of the homecharacteristic data with the highest weight; and/or (ii) display theinfluential home characteristic factors to the user.

In some embodiments, the home modification indication may include atleast one of: completion of a home maintenance learning module,performance of maintenance on a component of the property, an averagepower consumption for the property, an average water consumption for theproperty, and an indication of average occupancy. Generating the homescore may include anonymizing the home score such that anonymizedunderwriting can be performed using the anonymized home score.

In another aspect, a computer-implemented method for evaluating a scoreand recommending modifications for a property by a user may be provided.The method may be implemented via one or more local or remoteprocessors, servers, sensors, transceivers, memory units, and/or otherelectronic or electrical components. The method may include: (1)retrieving, by one or more processors, home data for a property and userdata for a user; (2) determining, by the one or more processors andusing a trained machine learning evaluation model, one or more homescore factors based upon at least the home data; (3) generating, by theone or more processors and based upon the one or more home scorefactors, a home score for the property; and/or (4) generating, by theone or more processors and based upon the user data and the home score,one or more recommended home modifications, wherein the one or morerecommended home modifications would, if implemented, cause amodification at least one of the one or more home score factors. Themethod may include additional, less, or alternative actions, includingthose discussed elsewhere herein.

For instance, in some embodiments the method may include determining,based upon the user data, previous home characteristic data. The one ormore home score factors may include one or more difference factorsdifferent between the previous home characteristic data and the homedata. In further embodiments, the one or more recommended homemodifications may be based upon the one or more difference factors.

In some embodiments, the one or more difference factors include at leastone of: differences in environment, differences in construction codes,differences in weather, differences in landscape, or differences inwildlife.

In some embodiments, at least some of the home data is retrieved fromone or more smart devices on the property and the home data may includeat least one of: location data, environment data, first responder data,home structure data, and adherence to local construction codes.

In further embodiments, determining the one or more home score factorsmay include weighting the home data and the user data, and thenon-transitory computer-readable medium further stores instructionsthat, when executed by the one or more processors, cause the computingdevice to (i) determine influential home characteristic factors, whereinthe influential home characteristic factors are a subset of the homecharacteristic data with the highest weight; and/or (ii) display theinfluential home characteristic factors to the user, such as on the usermobile device.

This summary is provided to introduce a selection of concepts in asimplified form that are further described in the Detailed Descriptions.This summary is not intended to identify key features or essentialfeatures of the claimed subject matter, nor is it intended to be used tolimit the scope of the claimed subject matter.

Advantages will become more apparent to those of ordinary skill in theart from the following description of the preferred aspects, which havebeen shown and described by way of illustration. As will be realized,the present aspects may be capable of other and different aspects, andtheir details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary computing system that facilitates retrievinghome data from a property, smart device, and/or mobile device, as wellas evaluating and generating a home score from home data collected bythe system and/or stored on a server.

FIG. 2 depicts an exemplary architecture for a mobile device, computingdevice, or smart device of FIG. 1 .

FIG. 3 depicts an exemplary interface for depicting and displaying ahome score and improvements for a property, as well as related homescore factors that influence the overall home score in the network ofFIG. 1 .

FIG. 4 depicts an exemplary interface for depicting and displaying amoving home score for a property, as well as related home score factorsthat influence the overall home score in the network of FIG. 1 .

FIG. 5 depicts an exemplary interface for submitting proof of amaintenance task performed for a property, as well as displayingresulting modifications to a home score and related home score factorsthat influence the overall home score in the network of FIG. 1 .

FIG. 6 depicts a flow diagram representing an exemplarycomputer-implemented method for evaluating and analyzing home databefore generating a modified home score based upon the home data.

FIG. 7 depicts a flow diagram representing an exemplarycomputer-implemented method for evaluating home data before generatingrecommended modifications based upon a home score for the property.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

Techniques, systems, apparatuses, components, devices, and methods aredisclosed for evaluating and generating a home score and modificationsfor a property. For example, a system may use a machine learning modelto evaluate data related to the property and/or user, and identify datarelated to characteristics of the property and/or a likelihood of lossassociated with the property. The model may then use the characteristicdata and/or likelihood of loss to determine relevant factors to theproperty and subsequently calculate a home score. In some scenarios, thesystem may further generate recommended modifications to perform basedupon the home score and/or user data. In further scenarios, a device maydisplay the calculated home score and/or modification recommendations toa user moving to a new home and/or looking to perform maintenance on ahome. In some such embodiments, the device may further display relevantfactors and/or characteristics of the property in addition to thecalculated home score and/or recommendations.

When moving to a new property and/or performing maintenance on aproperty, homeowners and/or property owners may benefit from additionalinformation to facilitate decisions and/or actions. However, suchhomeowners and/or property owners may be unable to readily access someof the potentially useful information that would influence such adecision. While the data exists, much of said data is either difficultfor an individual to gather, generally held private, and/or the use ofthe data is not apparent.

By training and/or using a machine learning model trained on home and/orproperty data to evaluate such data, a system can collect and analyzelarge quantities of data to determine what data is relevant to apotential decision. Moreover, by training and/or using a machinelearning data evaluation model, the system can identify otherwiseinvisible trends and relations between characteristics and potentiallyimpactful factors, such as the risk inherent in particular buildingmaterials and/or designs. As such, the system may generate a home score,modifications to a home score, and/or recommended actions based upon thecharacteristics of a property, the likelihood of loss and/or risk, aswell as the various identified trends and relationships with homefactors.

More specifically, the system may generate the home score and/orrecommended actions based upon factors such as environmental data,location data, first responder data, home structure data, occupancydata, usage data, user data, and/or likelihood of loss data. In someembodiments, the system retrieves and analyzes home data and/or userdata using a machine learning model to determine and/or weight therelevant factors. In further embodiments, the system scores the factorsin determining an overall home score for the property in question.

In some embodiments, the system may display and/or cause a computingdevice to display the home score to a user. In further embodiments, thesystem may similarly display and/or cause a computing device to displayhome score factors and/or characteristic data to a user in addition tothe home score and/or recommended actions. Depending on the embodiment,the system may calculate the home score depending on different factors.For example, the system may calculate a home score to show to a userpotential or planned maintenance on a house differently than the systemmay calculate a home score for a user moving to the house. Similarly,the system may display and/or cause a computing device to display thedifferent home scores, recommended actions, home score factors, and/orhome characteristic data depending on the application. In furtherembodiments, the system may display and/or cause a computing device todisplay the home score and/or recommended actions to the user inresponse to receiving an indication and/or request from the user.

The present embodiments relate to computing systems andcomputer-implemented methods for evaluating and generating a home scorefor a property. The property may be a house, an office building, anapartment, a condominium, a home extension, a garage, a deck, an emptyplot, or any other such property which a user would potentiallypurchase, build, and/or otherwise develop.

Exemplary System for Calculating a Home Score and Modifications

FIG. 1 depicts an exemplary system 100 for calculating a home score fora property. Depending on the embodiment, the system 100 may calculate ahome score, a moving home score, a maintenance home score, or any othersimilar home score for a user. In further embodiments, the system 100may determine recommended actions to modify or improve the home score aswell. An entity (e.g., requestor 114), such as a user or an insurancecompany, may wish to calculate and/or view any such home score orrecommended action for a real property (e.g., property 116).

Additionally, the property (e.g., property 116) and, more specifically,a computing device 117 associated with the property 116, a smart device110 within the property 116, and/or one or more mobile devices maydetect, gather, or store home data (e.g., home telematics data)associated with the functioning, operation, and/or evaluation of theproperty 116. The computing device 117 associated with the property 116may transmit home telematics data in a communication 196 via the network130 to a request server 140.

In some embodiments, the request server 140 may already store home data(e.g., home telematics data) and/or user data (e.g., user telematicsdata) in addition to any received home telematics data or usertelematics data. Further, the request server 140 may use the hometelematics data and/or user telematics data to evaluate and calculate ahome score for the property 116. Additionally or alternatively, one ormore mobile devices (e.g., mobile device 112) communicatively coupled tothe computing device associated with the property 116 may transmit hometelematics data and/or user telematics data in communication 192 to therequest server 140 via the network 130.

The smart device 110 may include a processor, a set of one or severalsensors 120, and/or a communications interface 118. In some embodiments,the smart device 110 may include single devices, such as a smarttelevision, smart refrigerator, smart doorbell, or any other similarsmart device. In further embodiments, the smart device 110 may include anetwork of devices, such as a security system, a lighting system, or anyother similar series of devices communicating with one another. The setof sensors 120 may include, for example, a camera or series of cameras,a motion detector, a temperature sensor, an airflow sensor, a smokedetector, a carbon monoxide detector, or any similar sensor.

Although FIG. 1 depicts the set of sensors 120 inside the smart device110, it is noted that the sensors 120 need not be internal components ofthe smart device 110. Rather, a property 116 may include any number ofsensors in various locations, and the smart device 110 may receive datafrom these sensors during operation. In further embodiments, thecomputing device 117 associated with the property 116 may receive datafrom the sensors during operation. In still further embodiments, thecomputing device 117 associated with the property 116 may be the smartdevice 110.

The communications interface 118 may allow the smart device 110 tocommunicate with the mobile device 112, the sensors 120, and/or acomputing device 117 associated with the property 116. Thecommunications interface 118 may support wired or wirelesscommunications, such as USB, Bluetooth, Wi-Fi Direct, Near FieldCommunication (NFC), etc. The communications interface 118 may allow thesmart device 110 to communicate with various content providers, servers,etc., via a wireless communication network such as a fifth-, fourth-, orthird-generation cellular network (5G, 4G, or 3G, respectively), a Wi-Finetwork (802.11 standards), a WiMAX network, a wide area network (WAN),a local area network (LAN), etc. The processor may operate to formatmessages transmitted between the smart device 110 and the mobile device112, sensors 120, and/or computing device 117 associated with theproperty 116; process data from the sensors 120; transmit communicationsto the request server 140; etc.

In some embodiments, the smart device 110 may collect the hometelematics data using the sensors 120. Depending on the embodiment, thesmart device may collect home telematics data regarding the usage and/oroccupancy of the property. In some embodiments, the home telematics datamay include data such as security camera data, electrical system data,plumbing data, appliance data, energy data, maintenance data, guestdata, homeshare data, and any other suitable data representative ofproperty 116 occupancy and/or usage.

For instance, the home telematics data may include data gathered frommotion sensors and/or images of the home from which it may be determinedhow many people occupy the property and the amount of time they eachspend within the home. Additionally or alternatively, the hometelematics data may include electricity usage data, water usage data,HVAC usage data (e.g., how often the furnace or air conditioner unit ison), and smart appliance data (e.g., how often the stove, oven, dishwasher, or clothes washer is operated). The home telematics data mayalso include home occupant mobile device data or home guest mobiledevice data, such as GPS or other location data.

The user data (e.g., user telematics data) may include data from theuser's mobile device, or other computing devices, such as smart glasses,wearables, smart watches, laptops, etc. The user data or user telematicsdata may include data associated with the movement of the user, such asGPS or other location data, and/or other sensor data, including cameradata or images acquired via the mobile or other computing device. Insome embodiments, the user data and/or user telematics data may includehistorical data related to the user, such as historical home data,historical claim data, historical accident data, etc. In furtherembodiments, the user data and/or user telematics data may includepresent and/or future data, such as expected home data when moving,projected claim data, projected accident data, etc. Depending on theembodiment, the historical user data and the present and/or future datamay be related.

The user data or user telematics data may also include vehicletelematics data collected or otherwise generated by a vehicle telematicsapp installed and/or running on the user's mobile device or othercomputing device. For instance, the vehicle telematics data may includeacceleration, braking, cornering, speed, and location data, and/or otherdata indicative of the user's driving behavior.

The user data or user telematics data may also include home telematicsdata collected or otherwise generated by a home telematics app installedand/or running on the user's mobile device or other computing device.For instance, a home telematics app may be in communication with a smarthome controller and/or smart appliances or other smart devices situatedabout a home, and may collect data from the interconnected smart devicesand/or smart home sensors. Depending on the embodiment, the usertelematics data and/or the home telematics data may include informationinput by the user at a computing device or at another device associatedwith the user. In further embodiments, the user telematics data and/orthe home telematics data may only be collected or otherwise generatedafter receiving a confirmation from the user, although the user may notdirectly input the data.

Mobile device 112 may be associated with (e.g., in the possession of,configured to provide secure access to, etc.) a particular user, who maybe an owner of a property, such as property 116. In further embodiments,the mobile device 112 may be associated with a potential homeowner,shopper, developer, or other such particular user. Mobile device 112 maybe a personal computing device of that user, such as a smartphone, atablet, smart glasses, smart headset (e.g., augmented realty, virtualreality, or extended reality headset or glasses), wearable, or any othersuitable device or combination of devices (e.g., a smart watch plus asmartphone) with wireless communication capability. In the embodiment ofFIG. 1 , mobile device 112 may include a processor 150, a communicationsinterface 152, sensors 154, a memory 170, and a display 160.

Processor 150 may include any suitable number of processors and/orprocessor types. Processor 150 may include one or more CPUs and one ormore graphics processing units (GPUs), for example. Generally, processor150 may be configured to execute software instructions stored in memory170. Memory 170 may include one or more persistent memories (e.g., ahard drive and/or solid state memory) and may store one or moreapplications, including report application 172.

The mobile device 112 may be communicatively coupled to the smart device110, the sensors 120, and/or a computing device 117 associated with theproperty 116. For example, the mobile device 112 and the smart device110, sensors 120, and/or computing device 117 associated with theproperty 116 may communicate via USB, Bluetooth, Wi-Fi Direct, NearField Communication (NFC), etc. For example, the smart device 110 maysend home telematics data, user telematics data, or other sensor data inthe property 116 via communications interface 118 and the mobile device112 may receive the home telematics data or other sensor data viacommunications interface 152. In other embodiments, mobile device 112may obtain the home telematics data from the property 116 from sensors154 within the mobile device 112.

Further still, mobile device 112 may obtain the home telematics dataand/or user telematics data via a user interaction with a display 160 ofthe mobile device 112. For example, a user may take a photographindicative of a property and/or input information regarding acharacteristics indicative of potential hazards or other such home scorefactors associated with the property 116 at the display 160. Scoringunit 174 may be configured to prompt a user to take a photograph orinput information at the display 160. The mobile device 112 may thengenerate a communication that may include the home telematics dataand/or user telematics data and may transmit the communication 192 tothe request server 140 via communications interface 152.

In some embodiments, the scoring application 172 may include or may becommunicatively coupled to a home score application or website. In suchembodiments, the request server 140 may obtain the home telematics dataand/or user telematics data via stored data in the home scoreapplication or via a notification 176 in the scoring application 172granting the scoring application 172 access to the home scoreapplication data.

Depending on the embodiment, a computing device 117 associated with theproperty 116 may obtain home telematics data for the property 116indicative of environmental conditions, housing and/or constructionconditions, location conditions, first responder conditions, or othersimilar metrics of home telematics data. The computing device 117associated with the property 116 may obtain the home telematics datafrom one or more sensors 120 within the property 116. In otherembodiments, the computing device 117 associated with the property 116may obtain home telematics data through interfacing with a mobile device112.

Depending on the embodiment, home telematics data may be indicative ofboth visible and invisible hazards to the property. For example, thehome telematics data may include image data of the property 116 as wellas internal diagnostic data on functionality of particular devices orcomponents of the property 116. In another example, home telematics datamay be used to determine that the property 116 and/or components of theproperty 116 are likely to require repair and/or replacement, and maylead to a potential risk or claim associated with the property 116.

In some embodiments, the home telematics data may includeinterpretations of raw sensor data, such as detecting an intruder eventwhen a sensor detects motion during a particular time period. Thecomputing device 117 associated with the property 116, mobile device112, and/or smart device 110 may collect and transmit home telematicsdata to the request server 140 via the network 130 in real-time or atleast near real-time at each time interval in which the system 100collects home telematics data. In other embodiments, a component of thesystem 100 may collect a set of home telematics data at several timeintervals over a time period (e.g., a day), and the smart device 110,computing device 117 associated with the property 116, and/or mobiledevice 112 may generate and transmit a communication which may includethe set of home telematics data collected over the time period.

Also, in some embodiments, the smart device 110, computing device 117associated with the property 116, and/or mobile device 112 may generateand transmit communications periodically (e.g., every minute, everyhour, every day), where each communication may include a different setof home telematics data and/or user telematics data collected over themost recent time period. In other embodiments, the smart device 110,computing device 117 associated with the property 116, and/or mobiledevice 112 may generate and transmit communications as the smart device110, mobile device 112, and/or computing device 117 associated with theproperty 116 receive new home telematics data and/or user telematicsdata.

In further embodiments, a trusted party may collect and transmit thehome telematics data and/or user telematics data, such as an evidenceoracle. The evidence oracles may be devices connected to the internetthat record and/or receive information about the physical environmentaround them, such as a smart device 110, a mobile device 112, sensors120, a request server 140, etc. In further examples, the evidenceoracles may be devices connected to sensors such as connected videocameras, motion sensors, environmental conditions sensors (e.g.,measuring atmospheric pressure, humidity, etc.) as well as otherInternet of Things (IoT) devices.

The data may be packaged into a communication, such as communication 192or 196. The data from the evidence oracle may include a communicationID, an originator (identified by a cryptographic proof-of-identity,and/or a unique oracle ID), an evidence type, such as video and audioevidence, and a cryptographic hash of the evidence. In anotherembodiment, the evidence is not stored as a cryptographic hash, but maybe directly accessible by an observer or other network participant.

Next, the smart device 110 and/or computing device 117 associated withthe property 116 may generate a communication 196 including arepresentation of the home telematics data wherein the communication 196is stored at the request server 140 and/or an external database (notshown).

In some embodiments, generating the communication 196 may includeobtaining identity data for the smart device 110, computing device 117,and/or the property 116; obtaining identity data for the mobile device112 in the property 116; and/or augmenting the communication 196 withthe identity data for the smart device 110, the property 116, thecomputing device 117, and/or the mobile device 112. The communication196 may include the home telematics data or a cryptographic hash valuecorresponding to the home telematics data.

In some embodiments, the mobile device 112 or the smart device 110 maytransmit the home telematics data and/or user telematics data to arequest server 140. The request server 140 may include a processor 142and a memory that stores various applications for execution by theprocessor 142. For example, a score calculator 144 may obtain hometelematics data for a property 116 and/or user telematics data for auser to analyze, calculate, and/or determine a risk, home score factor,recommended action, or home score for a property 116 during a particulartime period in response to a calculation request 194, as described inmore detail below with regard to FIG. 6 .

In further embodiments, a requestor 114 may transmit a communication 194including a score calculation request to the request server 140 via thenetwork 130. Depending on the embodiment, the requestor may include oneor more processors 122, a communications interface 124, a request module126, a notification module 128, and a display 129. In some embodiments,each of the one or more processors 122, communications interface 124,request module 126, notification module 128, and display 129 may besimilar to the components described above with regard to the mobiledevice 112.

Depending on the embodiment, the requestor 114 may be associated with aparticular user, such as a shopper, a home shopping website and/orapplication, a home rental website and/or application, a constructioncompany, a real estate company, an underwriting company, an insurancecompany, etc. In some embodiments, the requestor 114 may be associatedwith the same user as the request server 140. In other embodiments, therequestor 114 is associated with a different user than the requestserver 140. In some such embodiments, the request module 126 and/ornotification module 128 may include or be part of a request application,such as an underwriting application, a shopping application, aninsurance application, etc.

In some embodiments, the requestor 114 may transmit a communication 194including a score request to the requestor 140 via the communicationsinterface 124. In some such embodiments, the requestor 114 may requestthe score to use as an input to a rating model, an underwriting model, aclaims generation model, or any other similarly suitable model. Forexample, the requestor 114 may request the score to use to determine apotential risk for a property. As another example, the requestor 114 mayrequest multiple scores to determine potential hazards with regard tobuilding types.

Exemplary Home Score Factors

In some embodiments, the home score calculation may include acalculation for home score factors, such as (i) an environment score;(ii) a location score; (iii) a first responder score; (iv) aconstruction score; (v) a usage score; (vi) an occupancy score, and/or(v) a risk score. Depending on the embodiment, the environment score maybe representative of environmental hazards and/or benefits. For example,the environment score may be representative of weather, temperature,seasonal hazards and/or changes, local fauna, local flora, air quality,pollen, landscape, bodies of water, and any other such suitableenvironmental hazards and/or benefits.

The location score may be representative of location-based hazardsand/or benefits. For example, the location score may be representativeof local population density, local classification (e.g., urban, rural,suburban, city, town, village, etc.), proximity to a highway, proximityto public transportation, proximity to various businesses, proximity toneighbors, proximity to schools, crime rates, and any other suchsuitable location-based hazards and/or benefits.

The first responder score may be representative of accessibility tofirst responders in emergency events. For example, the first responderscore may be representative of proximity to a hospital, proximity to afire station, proximity to a police station, presence of nearby firehydrants, ease of ambulance access, crime response rate, crime responsetime and/or speed, and any other such suitable hazards and/or benefits.

The construction score may be representative of hazards and/or benefitsrelated to the construction of a house or other item on the property.For example, the construction score may be representative of adherenceto construction codes, adherence to construction best practices,building materials used, structural stability, architectural design,house age, history of replacements and/or repairs, appliances, smartdevices, plumbing, water consumption, power consumption, wiring,security, and any other such suitable hazards and/or benefits.

Similarly, the usage score may be representative of hazards and benefitsrelated to the usage of the property, and the occupancy score may berepresentative of hazards and benefits related to the occupancy of theproperty.

In some embodiments, the risk score may be representative of a level ofrisk related to the property. The level of risk calculation may includea determination as to past or potential claim damage and/or severity ofclaim damage. In some embodiments, the level of risk may refer to alevel of risk for a particular time period.

Additionally or alternatively, the level of risk may include adetermination of a quote or cost associated with the level of risk forthe particular time period. In still further embodiments, the level ofrisk may include a determination of a quote or cost associated with thelevel of risk for a longer period of time, such as a month, year, etc.In further embodiments, the level of risk may depend on additionalfactors, such as type of claim, cost of claim, cause of the claim,confirmation of fault, liability amount paid out, property damage paidout, freeform data (need to understand that from a data perspective, soneeds other processing), whether coverage is paid, catastrophe, bodilyinjury, repair costs, estimated values for items damaged, prior damage,claim subrogation status, location of loss, date of loss, time of loss,date the claim was reported, etc.

It will be understood that, in some embodiments, some home telematicsdata and/or user telematics data may influence multiple home scorefactors as described above. In some such embodiments, the system 100 mayonly apply the home telematics data and/or user telematics data to thefactor most influenced by the data in question. In other embodiments,the system 100 applies the home telematics data and/or user telematicsdata to all potential categories. In still other embodiments, the system100 applies the home telematics data and/or user telematics data to afirst factor and then, based upon the application to the first factor,determines not to apply the home telematics data and/or user telematicsdata to any other factors.

Moreover, the home score provides a benefit through increased securityand privacy, as the score reduces risk of reverse engineering privatedetails. Notably, by calculating the home score, the system 100 allowsfor public disclosure of important and/or useful data without risk ofindividual characteristics or factors becoming known. For example, ahome score of 78 out of 100 for a property would allow a useful metricto a potential buyer or group using the home score for underwriting, butwould not provide access to the information underlying the score. Forexample, an owner of a property may prefer to keep details regardinginsurance claims private, but may still need to assure a potential buyerregarding the home.

In some embodiments, the home score depends on at least one of type ofclaim, cost of claim, cause of the claim, confirmation of fault,liability amount paid out, property damage paid out, freeform data (needto understand that from a data perspective, so needs other processing),whether coverage is paid, catastrophe, bodily injury, repair costs,estimated values for items damaged, prior damage, claim subrogationstatus, location of loss, date of loss, time of loss, date the claim wasreported, etc. None of the information used to generate the home scoreis visible, however, allowing for greater privacy and security. As such,the system 100 may anonymize the home score such that anonymizedunderwriting can be performed using the anonymized home score in thatthe underlying information is kept unknown to the underwriter.

In some embodiments, a mobile device 112 may stream the home telematicsdata and/or user telematics data to the request server 140 via thenetwork 130 in real or near-real time. For example, the mobile deviceand/or a scoring application 172 on the mobile device 112 may update therequest server 140 via the network 130 whenever a new event occurs withregard to home telematics data and/or user telematics data. In furtherembodiments, the mobile device 112 may receive confirmations of updatedinformation and may notify the user that the mobile device 112 hasupdated the request server 140 via the network 130.

Exemplary Machine Learning

Optionally, the system 100 may determine home characteristic data and/ora level of risk from the home telematics data and/or user telematicsdata using a machine learning model for data evaluation. The machinelearning model may be trained based upon a plurality of sets of hometelematics data and/or user telematics data and corresponding homecharacteristic data and/or levels of risk. The machine learning modelmay use the home telematics data and/or user telematics data to generatethe home characteristic data and/or level of risk. In some embodiments,the machine learning model may use the home characteristic data and/orlevel of risk to generate the home score factors and/or the home score.In still further embodiments, the machine learning model may use thehome score factors to generate the home score.

Machine learning techniques have been developed that allow parametric ornonparametric statistical analysis of large quantities of data. Suchmachine learning techniques may be used to automatically identifyrelevant variables (i.e., variables having statistical significance or asufficient degree of explanatory power) from data sets. This may includeidentifying relevant variables or estimating the effect of suchvariables that indicate actual observations in the data set. This mayalso include identifying latent variables not directly observed in thedata, viz. variables inferred from the observed data points.

In some embodiments, the methods and systems described herein may usemachine learning techniques to identify and estimate the effects ofobserved or latent variables such as weather, temperature, seasonalhazards and/or changes, local fauna, local flora, air quality, pollen,landscape, bodies of water, local population density, localclassification (e.g., urban, rural, suburban, city, town, village,etc.), proximity to a highway, proximity to public transportation,proximity to various businesses, proximity to neighbors, proximity toschools, crime rates, proximity to a hospital, proximity to a firestation, proximity to a police station, presence of nearby firehydrants, ease of ambulance access, crime response rate, crime responsetime and/or speed, adherence to construction codes, adherence toconstruction best practices, building materials used, structuralstability, architectural design, house age, history of replacementsand/or repairs, appliances, smart devices, plumbing, water consumption,power consumption, wiring, security, type of claim, cost of claim, causeof the claim, confirmation of fault, liability amount paid out, propertydamage paid out, freeform data (need to understand that from a dataperspective, so needs other processing), coverage is paid, catastrophe,bodily injury, repair costs, estimated values for items damaged, priordamage, claim subrogation status, location of loss, date of loss, timeof loss, date the claim was reported, etc.

Some embodiments described herein may include automated machine learningto determine risk levels, identify relevant risk factors, evaluate hometelematics data and/or user telematics data, identify environmental riskfactors, identify locale-based risk factors, identify home structurerisk factors, identify first responder-based risk factors, identifyoccupancy risk factors, identify usage risk factors, calculate anenvironmental score, calculate a location score, calculate a homestructure score, calculate a first responder score, calculate anoccupancy score, calculate a usage score, calculate an overall homescore, and/or perform other functionality as described elsewhere herein.

Although the methods described elsewhere herein may not directly mentionmachine learning techniques, such methods may be read to include suchmachine learning for any determination or processing of data that may beaccomplished using such techniques. In some embodiments, suchmachine-learning techniques may be implemented automatically uponoccurrence of certain events or upon certain conditions being met. Useof machine learning techniques, as described herein, may begin withtraining a machine learning program, or such techniques may begin with apreviously trained machine learning program.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data (suchas customer financial transaction, location, browsing or onlineactivity, mobile device, vehicle, and/or home sensor data) in order tofacilitate making predictions for subsequent customer data. Models maybe created based upon example inputs of data in order to make valid andreliable predictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as mobile device, server, or home system sensor and/or controlsignal data, and other data discussed herein. The machine learningprograms may utilize deep learning algorithms that are primarily focusedon pattern recognition, and may be trained after processing multipleexamples. The machine learning programs may include Bayesian programlearning (BPL), voice recognition and synthesis, image or objectrecognition, optical character recognition, and/or natural languageprocessing, either individually or in combination. The machine learningprograms may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct or a preferredoutput. In unsupervised machine learning, the processing element may berequired to find its own structure in unlabeled example inputs. In oneembodiment, machine learning techniques may be used to extract thecontrol signals generated by computer systems or sensors, and under whatconditions those control signals were generated.

The machine learning programs may be trained with smart device-mounted,home-mounted, and/or mobile device-mounted sensor data to identifycertain home data, such as analyzing home telematics data and/or usertelematics data to identify and/or determine environmental data,location data, first responder data, home structure data, occupancydata, usage data, an overall home score, and/or other such potentiallyrelevant data.

After training, machine learning programs (or information generated bysuch machine learning programs) may be used to evaluate additional data.Such data may be related to publically accessible data, such as buildingpermits and/or chain of title. Other data may be related toprivately-held data, such as insurance and/or claims information relatedto the property and/or items associated with the property. The trainedmachine learning programs (or programs utilizing models, parameters, orother data produced through the training process) may then be used fordetermining, assessing, analyzing, predicting, estimating, evaluating,or otherwise processing new data not included in the training data. Suchtrained machine learning programs may, therefore, be used to performpart or all of the analytical functions of the methods describedelsewhere herein.

The mobile device 112 and the computing device 117 associated with theproperty 116 may be associated with the same user. Mobile device 112,and optionally the computing device 117 associated with the property116, may be communicatively coupled to requestor 114 via a network 130.Network 130 may be a single communication network, or may includemultiple communication networks of one or more types (e.g., one or morewired and/or wireless local area networks (LANs), and/or one or morewired and/or wireless wide area networks (WANs) such as the internet).In some embodiments, the requestor 114 may connect to the network 130via a communications interface 124 much like mobile device 112.

While FIG. 1 shows only one mobile device 112, it is understood thatmany different mobile devices (of different users), each similar tomobile device 112, may be in remote communication with network 130.Additionally, while FIG. 1 shows only one property 116 and associatedcomputing device 117, it is understood that many different entitylocations, each similar to property 116, may include computing devices117 that are in remote communication with network 130.

Further, while FIG. 1 shows only one requestor, 114, it is understoodthat many different requestors, each similar to requestor 114, may be inremote communication with network 130. Requestor 114 and/or any othersimilar requestor may be associated with an insurance company, aregulator organization, a property rental company, and/or a similarorganization.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

Exemplary Architecture for a Computing Device Transmitting Data to beAnalyzed

Referring next to FIG. 2 , it should be appreciated that while FIG. 2depicts a smart device 110 and/or mobile device 112 with onemicroprocessor 210, the controller 204 may include multiplemicroprocessors 210. Additionally, the memory of the controller 204 mayinclude multiple RAMs 212 and multiple program memories 208. Further,although FIG. 2 depicts the I/O circuit 216 as a single block, the I/Ocircuit 216 may include a number of different types of I/O circuits. Forexample, the controller 204 may implement the RAM 212 and the programmemory 208 as semiconductor memory, magnetically readable memory, oroptically readable memory.

The one or more processors 210 may be adapted and configured to executeany one of the plurality of software applications 230 or any of theplurality of software routines 240 residing in the program memory 204 orelsewhere. One of the plurality of applications 230 may include a homescoring application 232 that may be implemented as a series ofmachine-readable instructions for performing the various tasksassociated with implementing one or more of the operation featuresaccording to the home scoring application.

Another of the plurality of applications 230 may include a shopping homescore application 234 that may be implemented as a series ofmachine-readable instructions. Another application of the plurality ofapplications 230 may include a building and/or property development homescore application 236 that may be implemented as a series ofmachine-readable instructions.

Another application of the plurality of applications 230 may include ahome characteristic data and/or level of risk evaluator 238 that may beimplemented as a series of machine-readable instructions. Depending onthe embodiments, the plurality of software applications 230 may notperform the actual calculations, but instead facilitate the transfer ofhome telematics data and/or user telematics data and the results of anycalculations between the smart device 110 and/or mobile device 112 andthe request server 140 by way of the network 130.

The plurality of software applications 230 may cooperate with any of theplurality of software routines 240 to perform functions relating toanalysis, evaluation, and/or scoring of home telematics data and/or usertelematics data. In some embodiments, one of the plurality of softwareroutines 240 may be a home characteristic data routine 242 thatdetermines and/or generates home characteristic data from hometelematics data and/or user telematics data.

Another of the plurality of software routines may be a level of riskand/or risk score routine 244 that determines and/or generates a levelof risk and/or a risk score from the home telematics data and/or usertelematics data. Another of the plurality of software routines 240 maybe a home score factor route 246 to generate a home score from the homecharacteristic data and/or the risk score.

Still another of the plurality of software routines 240 may be areporting routine 248 that reports the home telematics data and/or usertelematics data to the request server 140 via the network 130.Similarly, one of the plurality of software routines 240 may be a hometelematics data and/or user telematics data gathering routine 250 thatgathers the home telematics data and/or user telematics data from thesmart device 110 and/or mobile device 112. Depending on the embodiment,the plurality of software routines 240 additionally or alternativelycauses the request server 140 or sensors 120 to perform functions inaddition to or in place of the smart device 110 and/or mobile device112.

Any of the plurality of software routines 240 may be designed to operateindependently of the software applications 230 or in conjunction withthe software applications 230 to implement modules associated with themethods discussed herein using the microprocessor 210 of the controller204. Additionally, or alternatively, the software applications 230 orsoftware routines 240 may interact with various hardware modules thatmay be installed within or connected to the mobile device 112 or thesmart device 110. Such modules may implement some or all of the variousexemplary methods discussed herein or other related embodiments.

For instance, such modules may include a module for gathering hometelematics data and/or user telematics data from sensors 120, a modulefor transmitting home telematics data and/or user telematics data to arequest server 140, a module for determining a likelihood of risk, amodule for calculating home score factors for a property 116, a modulefor calculating a risk score for a property 116, a module forcalculating an overall home score for a property, a module fordisplaying the home score for the property 116 to a user, and/or othermodules.

When gathering and/or transmitting home telematics data and/or usertelematics data, the controller 204 of the smart device 110 and/ormobile device 112 may implement a home telematics data and/or usertelematics data gathering module by one of the plurality of softwareapplications 230 to communicate with the sensors 120 to receive hometelematics data and/or user telematics data as described herein. In someembodiments, including external source communication via thecommunication unit 220, the controller 204 may further implement acommunication module based upon one of the plurality of softwareapplications 230 to receive information from external sources. Someexternal sources of information may be connected to the controller 204via the network 130, such as internet-connected third-party databases(not shown). Although the plurality of software applications 230 areshown as separate applications, it is to be understood that thefunctions of the plurality of software applications 230 may be combinedor separated into any number of the software applications 230 or thesoftware routines 240.

In some embodiments, the controller 204 may further implement areporting module by one of the plurality of software applications 230 tocommunicate home telematics data and/or user telematics data with therequest server 140. The home telematics data and/or user telematics datamay be received and stored by the request server 140, and the requestserver 140 may then use the home telematics data and/or user telematicsdata to calculate home characteristic data, level risk and/or riskscore, home score factors, and/or a home score. In some embodiments, thesmart device 110 and/or mobile device 112 then displays a home score toa user on a display 202.

Some example of sensors 120 operatively coupled to the mobile device 112and/or the smart device 110 include a GPS unit, an optical sensor, agyroscope, a microphone, an image capturing device, etc., which mayprovide information relating to the property 116 and relevant hometelematics data and/or user telematics data. In some specific instances,the sensors 120 may also be used to monitor power consumption, waterconsumption, temperature, wind pressure, power generation, etc. Itshould be appreciated that the aforementioned types of sensors andmeasurable metrics are merely examples and that other types of sensorsand measureable metrics are additionally envisioned.

Furthermore, the communication unit 220 may communicate with databases,other smart devices and/or mobile devices, or other external sources ofinformation to transmit and receive information relating to the homescore and home telematics data and/or user telematics data. Thecommunication unit 220 may communicate with the external sources via thenetwork 130 or via any suitable wireless communication protocol network,such as wireless telephony (e.g., GSM, CDMA, LTE, etc.), Wi-Fi (802.11standards), WiMAX, Bluetooth, infrared or radio frequency communication,etc. Additionally, the communication unit 220 may provide input signalsto the controller 204 via the I/O circuit 216. The communication unit220 may also transmit sensor data, device status information, controlsignals, and/or other output from the controller 204 to one or moreexternal sensors within the smart devices 110, mobile devices 112,and/or request servers 140.

The mobile device 112 and/or the smart device 110 may include auser-input device (not shown) for receiving instructions or informationfrom a user, such as settings relating to the home score generationfeatures. The user-input device (not shown) may include a “soft”keyboard that is presented on the display 202, an external hardwarekeyboard communicating via a wired or a wireless connection (e.g., aBluetooth keyboard), an external mouse, a microphone, or any othersuitable user-input device. The user-input device (not shown) may alsoinclude a microphone capable of receiving user voice input.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

Exemplary Home Score Modification and Recommendation Applications andInterfaces

FIG. 3 illustrates an interface 300 that displays a page 310 of anapplication or a website providing information for improving a house toa user. The page 310 may include a home score 320 and home score factors330 that contribute to the overall home score 320. Although FIG. 3illustrates four home score factors, it will be understood that a page310 may provide any suitable number of home factors 330, including noneor all applicable home factors 330. In some embodiments, the page 310may further include an indication of a particular improvement to theproperty 116 as well as links 340 and/or 350 to additional material forimproving the home score.

In some embodiments, the home score 320 is based upon the home scorefactors 330. In such embodiments, the system 100 may calculate the homescore factors 330 based upon home telematics data and/or user telematicsdata. In particular, the system 100 may retrieve home telematics datarelated to the property 116 and/or user telematics data related to theuser from one or more databases. In some embodiments, the one or moredatabases may be publically accessible databases, such as governmentdatabases, locale databases, weather databases, etc. In furtherembodiments, the one or more databases may additionally or alternativelybe privately accessible databases, such as insurance databases, hazarddatabases, construction databases, building databases, etc. Depending onthe embodiment, the system 100 then calculates the home score factorsbased upon the gathered data, as described in more detail with regard toFIG. 6 below.

Further, the interface 300 may display home characteristic datadetermined from the home telematics data and/or user telematics data asthe home score factor data. In some embodiments, the interface 300displays the home score factors that the system 100 determines to bemost relevant to the home score 320. In further embodiments, theinterface 300 displays the home score factors 330 that the system 100determines to be most relevant to the home score 320, and the interface300 further displays the home score factor data that is most relevant tothe chosen home score factors 330.

In some embodiments, the home score factors 330 may include individualscores related to each home score factor 330. Depending on theembodiment, the scores for the home score factors 330 may be scores usedto determine the home score 320. In other embodiments, the system 100may determine the home score 320 based upon the home score factors 330,but not necessarily on the displayed scores. In such embodiments, thesystem may calculate the displayed scores alongside the home score 320.

In further embodiments, the home score 320 and/or home score factors 330are a modified home score and modified home score factors, respectively.In such embodiments, the interface 300 may display a change in score inaddition to the home score 320 and/or home score factors. In otherembodiments, the interface 300 may display the old score and the newscore where appropriate.

In some embodiments, the links 340 and 350 may include links 340 forrecommended home modification actions and/or links 350 for generallessons and/or tips related to the property 116. In some embodiments,the links 340 and 350 are interchangeable and/or mixed. In otherembodiments, the links 340 and 350 are separated based upon whether therecommendations would modify the home score if implemented by the user.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

FIG. 4 illustrates an interface 400 that displays a page 410 of anapplication or a website providing information for user moving to aproperty 116. In particular, the page 410 provides an overall home score420 (in this case, 67.8) and home score factors 430. Although FIG. 4illustrates four home score factors, it will be understood that a page410 may provide any suitable number of home factors 430, including noneor all applicable home factors 430.

In some embodiments, the overall home score 420 is based upon the homescore factors 430. In such embodiments, the system 100 may calculate thehome score factors 430 based upon home telematics data. In particular,the system 100 may retrieve home telematics data related to the propertyfrom one or more databases. In some embodiments, the one or moredatabases may be publically accessible databases, such as governmentdatabases, locale databases, weather databases, etc. In furtherembodiments, the one or more databases may additionally or alternativelybe privately accessible databases, such as insurance databases, hazarddatabases, construction databases, building databases, etc.

Depending on the embodiment, the system 100 then calculates the homescore factors based upon the gathered home telematics data, as describedin more detail with regard to FIG. 6 below. In some embodiments, theinterface 400 may include only the home score 420 and/or home scorefactors 430 (e.g., as a pop-up or link on a webpage). In otherembodiments, the interface 400 may include other information relevant tothe property 116, such as the address, price, pictures of the house,company, etc.

In some embodiments, the interface 400 also displays a past/currentaddress 412 and an address 414 for the property 116. Depending on theembodiment, the past/current address 412 and the address 414 may includean exact address, a town, a state, a country, or any combinationthereof. In further embodiments, the past/current address 412 and theaddress 414 may include information as to the environment and/orlocation of the properties in question (e.g., rural, hilly area orurban, mountainous area). In further embodiments, the system 100 maydetermine differences between the past/current address 412 and theaddress 414. In such embodiments, the interface 400 may display links440 to important differences and/or important local information to theproperty 116.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

FIG. 5 illustrates an exemplary interface 500 that displays a page 510of an application or a website providing information for a house to auser. In particular, the page 510 provides an overall home score 520 (inthis case, 77.7) and home score factors 530. Although FIG. 5 illustratesfour home score factors, it will be understood that a page 510 mayprovide any suitable number of home factors 530, including none or allapplicable home factors 530. Moreover, the page 510 may further providea listed task 515 as well as links 540 to submit evidence of a repairand/or maintenance task. In such embodiments, a user may upload evidenceof a repair and/or maintenance task as an indication of a homemodification, which may modify the home score 520 and/or home scorefactors 530 as described herein. In some embodiments, the home score 520and/or home score factors 530 include a projected or actual modificationbased upon the repair in question.

In some embodiments, the home score 520 and/or home score factors 530are similar to the home score 320 and/or home score factors 330 asdescribed above with regard to FIG. 3 . Further, it will be understoodthat, although the overall home score 320, 420, and 520; home scorefactors 330, 430, and 530; and other such descriptions herein refer to a“home”, each may refer to land on a property, regardless of whetherdeveloped or undeveloped. As such, it will be understood that theembodiments discussed herein are not limited to physical buildings.

Further, it will be understood that, although FIGS. 3-5 depict mobiledevices and interfaces, depending on the embodiment, the system 100 maynotify a user through email, text, an application, a webpage, abrochure/newsletter, a phone call, or any other similar technique.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

Exemplary Methods for Evaluating Home Data to Generate a Home Score fora Property, Modifications, or Recommendations

FIG. 6 is a flow diagram of an exemplary computer-implemented method 600for evaluating and analyzing home data before generating a modified homescore based upon the home data (e.g., home telematics data). The method600 may be implemented by one or more processors of a computing systemsuch as request server 140 or mobile device 112. Alternatively oradditionally, the method 600 may be implemented by one or moreprocessors of a distributed system such as system 100 and/or variouscomponents of system 100 as described with regard to FIG. 1 above.

At block 602, the system 100 may retrieve home data (e.g., hometelematics data) for a property 116 and/or user data (e.g., usertelematics data) for a user. In some embodiments, the home telematicsdata may be data collected from one or more publically accessibledatabases, such as a government database, a weather database, a locationdatabase, etc. In further embodiments, the home telematics data may bedata collected from privately accessible databases with permission froman owner of the private database, such as accessing a risk profile froman insurance database, a hazard database, an environmental database,etc. In other embodiments, the system 100 may retrieve the hometelematics data from one or more smart devices, such as smart device110. In such embodiments, the home telematics data may be provided by aproperty owner who opts in to allow the smart device 110 to provide suchinformation.

In further embodiments, the system 100 may similarly gather the usertelematics data from a publically accessible database or a privatedatabase. Depending on the embodiment, the user may transmit the usertelematics data to the system 100 and/or transmit permission for thesystem 100 to access the user telematics data. In some embodiments, theuser telematics data may include past/current home data, past/currentclaims data, indicia of likelihood of risk for the user, etc.

In some embodiments, the system 100 may collect data from a smart device110 regarding the internal and/or external environment of the property116. In some embodiments, the system 100 may use data collected from thesmart device 110 and/or sensors 120 to determine a home usage (e.g.,frequency of use, wear, potential damage, need for maintenance, etc.)and/or a home occupancy (e.g., time spent away, time spent withutilities on/off, time with guests in residences, times with homeshareresidents in residence, etc.) for the property 116. In furtherembodiments, a mobile device 112 may additionally or alternativelycollect such information and/or the system 100 collects such informationfrom a mobile device 112.

In some embodiments, the system 100 may automatically detect routinemaintenance. In some embodiments, the system 100 may determine thatroutine maintenance occurs when components of the property 116 continuefunctioning above a predetermined threshold of efficiency and/orefficacy. In other embodiments, the system may determine that routinemaintenance occurs by cross-referencing data, such as permit data,sensor data, user input, uploaded financial data, etc. In furtherembodiments, the system 100 may notify a user about potential issuesthat may be addressed (e.g., in response to data such as from a smartdevice 110 and/or in response to a regular schedule/alarm), and thesystem may determine that maintenance has been performed in response toan indication from the user.

At block 604, the system 100 may determine, using a trained machinelearning evaluation model, one or more home score factors based upon atleast one of the home telematics data or the user telematics data. Insome embodiments, the system 100 may determine the one or more homescore factors by analyzing the home telematics data and/or usertelematics data using a machine learning model to determine homecharacteristic data for the property 116 or user characteristic data.Depending on the embodiment, the home characteristic data may be any oflocation data, environment data, first responder data, home structuredata, adherence to local construction codes, average power consumption,average water consumption, security data, occupancy data, detectedhazards, predicted hazards, alarm data, or any other similarly suitabledata for determining a home score. Similarly, the user characteristicdata may include past home characteristic data for properties owned orpreviously lived in by the user, claims arising from any hazardsassociated with properties associated with the user, a past home scorefor the user, or any other such data from the user.

In some embodiments, the system 100 may receive the home characteristicdata or user characteristic data outright in the form of home or usertelematics data and analyzes the received home or user characteristicdata to determine the factors. In other embodiments, the system 100 mayreceive larger quantities and/or ranges of home or user telematics dataand determines what data qualifies as home or user characteristic databefore discarding unimportant data. In some such embodiments, the system100 may make such a determination by using a trained machine learningmodel, as described herein.

In some embodiments, the system 100 may further analyze the hometelematics data using the trained machine learning data evaluation modelto determine a likelihood of loss associated with the property 116.Depending on the embodiment, the system 100 may determine the likelihoodof loss associated with the property based upon claims data in the homedata, such as type of claim, cost of claim, cause of the claim,confirmation of fault, liability amount paid out, property damage paidout, freeform data (need to understand that from a data perspective, soneeds other processing), coverage is paid, catastrophe, bodily injury,repair costs, estimated values for items damaged, prior damage, claimsubrogation status, location of loss, date of loss, time of loss, datethe claim was reported, etc.

Further, the system 100 may determine, based upon the homecharacteristic data for the property and/or the user characteristic datafor the user, the one or more home score factors. In embodiments inwhich the system 100 determines a likelihood of loss associated with theproperty, the system 100 may further determine the one or more homescore factors based upon the likelihood of loss in addition to or inplace of the home characteristic data and/or user characteristic data.In such embodiments, the one or more home score factors may include arisk score for the property 116, where the risk score may represent thepotential for a claim to occur with regard to the property 116. In suchembodiments, the system 100 may use the likelihood of loss and/or claimsdata pulled from an insurance database to anticipate the likelihood of aclaim using the machine learning model. In particular, the system 100may use either or both of individual claim data related to a particularproperty 116, or anonymized and/or historical claim data for a broaderclass of properties.

In such embodiments, the system 100 may train the machine learning modelby analyzing large quantities of home telematics data to determinewhether various characteristics of a property 116 make the property moreor less likely for a claim to occur. For example, the machine learningmodel of the system 100 may learn that houses closer to first respondersare more or less likely to be robbed. Similarly, the machine learningmodel of the system 100 may make similar determinations with regard tobuilding materials and/or durability, weather, the environment, age ofthe property, etc.

The system 100 may then use the machine learning model to calculate arisk score for the property 116 that anticipates the likelihood of aclaim occurring that relates to and/or arises from the property 116. Insome embodiments, the risk score may be a decimal from 0 to 1, a numberfrom 0 to 10, a number from 0 to 100, or any other suitable format for ascore.

At block 606, the system 100 may receive, from the user, a homemodification indication. Depending on the embodiment, the homemodification may be and/or may include any of an indication ofcompleting a learning module, an indication of repairing a component ofthe property 116, an indication of regular maintenance on the property116, an indication of a new building being built, an indication of apermit associated with the property 116, an indication of extremeweather, an indication of the user moving to a new property, anindication of differences between a past property of a user and theproperty 116, an indication of an addition of an extension to theproperty 116, an indication of a change in first responder presence nearthe property 116, an average power consumption for the property 116, anaverage water consumption for the property 116, an average occupancy forthe property 116, an average usage for the property 116, and any othersuitable indication that may affect a home score as described herein.

In some embodiments, the indication may be or include completion of alearning module. The learning module may educate a homeowner or buyer onhow to care for the new home and/or mitigate losses. The module mayinclude general homecare tips and/or tailored information for theparticular property 116. In further embodiments, the modules may includedifferences in coverage and what criteria would cause different coverageto be needed, as well as what the criteria may affect (such asautomobile insurance rates) and prices of such.

In other embodiments, the indication may be in response to anotification by the system 100 to repair or maintain a component of theproperty 116. In some such embodiments, the notification may include alist of potential vendors and/or companies to perform the repair and/ormaintenance. In further embodiments, the notification may include a linkto instructions to perform basic homecare tasks.

At block 608, the system 100 may modify, based upon the homemodification indication, at least one of the one or more home scorefactors to create one or more modified home score factors. In someembodiments, the system 100 may modify the home score factor(s) byfeeding the home modification indication and/or data associated with thehome modification indication into the machine learning model anddetermining a new output for each affected home score factor.

In further embodiments, the system 100 may modify the home scorefactor(s) by determining a change in the home score factor using themachine learning model, and subsequently applying the change to the homescore factor(s). In other embodiments, the system 100 may modify thehome score factor(s) collectively by determining the overall change inhome score according to the home modification indication. In suchembodiments, the system 100 may use the machine learning model todetermine the overall impact on the home score as reliant on the homescore factors directly.

At block 610, the system 100 may generate, based upon the one or moremodified home score factors, a home score for the property 116. In someembodiments, the system 100 may determine a particular type of homescore to generate for the property 116. For example, in someembodiments, the system 100 may determine the home score is amaintenance home score for a user maintaining the property 116. In otherembodiments, the system 100 may determine that the home score is amoving home score for a user looking to move to a property 116.

Depending on the embodiment, the system 100 may generate the home scorebased upon the determination. For example, the system 100 may weigh thehome score factors differently or use different home score factorsdepending on the determination. In further embodiments, the system 100makes a determination when analyzing the home data and/or whendetermining the home score factors at block 604.

In some embodiments, the system 100 may further determine influentialhome characteristic factors for the home score. Depending on theembodiment, the system 100 may determine the influential homecharacteristic factors based upon the weight assigned to each homefactor. For example, in some embodiments, the system 100 determines thetop 1, 5, 10, or any number of factors with the highest weight. In somesuch embodiments, the system 100 then displays and/or causes a computingdevice to display the influential home characteristic factors alongsidea home score.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

Next, FIG. 7 is a flow diagram of an exemplary computer-implementedmethod 700 for evaluating home data (e.g., home telematics data) anduser data (e.g., user telematics data), as well as generating a homescore for a property as well as one or more recommended homemodifications based upon the home score. The method 700 may beimplemented by one or more processors of a computing system such asrequest server 114 or mobile device 112. Alternatively or additionally,the method 700 may be implemented by one or more processors of adistributed system such as system 100 and/or various components ofsystem 100 as described with regard to FIG. 1 above.

At block 702, the system 100 may retrieve home telematics data and usertelematics data for a property 116, similar to block 602 above. At block704, the system 100 may determine one or more home score factors basedupon at least the home telematics data for the property. In someembodiments, the system 100 may determine the home score factors usingsimilar techniques as method 600. For example, depending on theembodiment, the system 100 may determine the home score factors byanalyzing the home telematics data for the property 116 to determinehome characteristic data and/or a likelihood of loss, similar to block604 in FIG. 6 above. Similarly, at block 706, the system 100 maygenerate a home score for the property 116 based upon the home scorefactors, similar to block 610 in FIG. 6 above.

At block 708, the system 100 may generate one or more recommended homemodifications based upon the user telematics data and the home score. Insome embodiments, the recommended home modifications would, ifimplemented, cause a modification of at least one of the one or morehome score factors. In further embodiments, the system 100 maydetermine, based upon the user telematics data, past property data forthe user. In such embodiments, the recommended home modifications may bebased upon differences between the past property for the user and theproperty 116.

Depending on the embodiment, the recommended home modifications may berecommendations for future or ongoing actions (e.g., a recommendation towatch for water filter failure) and/or for immediate actions (e.g., arecommendation to baby-proof the property). For example, in some suchembodiments, the recommended home modifications can be recommendationson differences between properties similar to the past property andproperty 116.

Depending on the embodiment, such differences may include differences inenvironment, wildlife, first responder time, home construction,location, or any other similar differences. For example, a user movingfrom a two story house in Indiana to a one story house in California mayneed to know about local water usage laws and/or different hazardsinherent in the new house layout.

In some embodiments, the system 100 may cause a computing device such asmobile device 112 to display the recommendations. In furtherembodiments, the system 100 may cause a computing device to displayevidence, support, and or potential methods of undertaking therecommendation as well. Depending on the embodiment, the recommendationmay include an offer for a different insurance plan and/or coveragedepending on the differences.

It will be understood that the above disclosure is one example and doesnot necessarily describe every possible embodiment. As such, it will befurther understood that alternate embodiments may include fewer,alternate, and/or additional steps or elements.

ADDITIONAL CONSIDERATIONS

With the foregoing, a user may opt-in to a rewards, insurance discount,or other type of program. After the insurance customer provides theiraffirmative consent, an insurance provider remote server may collectdata from the user's mobile device, vehicle, smart home, wearables,smart glasses, or other smart devices—such as with the customer'spermission or affirmative consent. The data collected may be related tohome telematics data, user telematics data, smart devices, accidentdata, and/or insured assets before (and/or after) an insurance-relatedevent, including those events discussed elsewhere herein. In return,risk averse insureds, homeowners, home builders, or other suchindividuals may receive discounts or insurance cost savings related topersonal articles, auto, and other types of insurance from the insuranceprovider.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

The systems and methods described herein are directed to an improvementto computer functionality, and improve the functioning of conventionalcomputers. Additionally, certain embodiments are described herein asincluding logic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a non-transitory, machine-readable medium) or hardware. In hardware,the routines, etc., are tangible units capable of performing certainoperations and may be configured or arranged in a certain manner. Inexample embodiments, one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘______’ ishereby defined to mean . . . ” or a similar sentence, there is no intentto limit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based upon any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this disclosureis referred to in this disclosure in a manner consistent with a singlemeaning, that is done for sake of clarity only so as to not confuse thereader, and it is not intended that such claim term be limited, byimplication or otherwise, to that single meaning.

The term “insurance policy,” “insurance plan,” or variations thereof asused herein, generally refers to a contract between an insurer and aninsured. In exchange for payments from the insured, the insurer pays fordamages to the insured which are caused by covered perils, acts orevents as specified by the language of the insurance policy. Thepayments from the insured are generally referred to as “premiums,” andtypically are paid on behalf of the insured upon purchase of theinsurance policy or over time at periodic intervals. The amount of thedamages payment is generally referred to as a “coverage amount” or a“face amount” of the insurance policy. An insurance policy may remain(or have a status or state of) “in-force” while premium payments aremade during the term or length of coverage of the policy as indicated inthe policy. An insurance policy may “lapse” (or have a status or stateof “lapsed”), for example, when the parameters of the insurance policyhave expired, when premium payments are not being paid, when a cashvalue of a policy falls below an amount specified in the policy (e.g.,for variable life or universal life insurance policies), or if theinsured or the insurer cancels the policy.

The terms “insurer,” “insuring party,” and “insurance provider” are usedinterchangeably herein to generally refer to a party or entity (e.g., abusiness or other organizational entity) that provides insuranceproducts, e.g., by offering and issuing insurance policies. Typically,but not necessarily, an insurance provider may be an insurance company.

Although the embodiments discussed herein relate to home insurancepolicies, it should be appreciated that an insurance provider may offeror provide one or more different types of insurance policies. Othertypes of insurance policies may include, for example, vehicle and/orautomobile insurance; homeowners insurance; condominium owner insurance;renter's insurance; life insurance (e.g., whole-life, universal,variable, term); health insurance; disability insurance; long-term careinsurance; annuities; business insurance (e.g., property, liability,commercial auto, workers compensation, professional and specialtyliability, inland marine and mobile property, surety and fidelitybonds); boat insurance; insurance for catastrophic events such as flood,fire, volcano damage and the like; motorcycle insurance; farm and ranchinsurance; personal article insurance; personal liability insurance;personal umbrella insurance; community organization insurance (e.g., forassociations, religious organizations, cooperatives); and other types ofinsurance products. In embodiments as described herein, the insuranceproviders process claims related to insurance policies that cover one ormore properties (e.g., homes, automobiles, personal articles), althoughprocessing other insurance policies is also envisioned.

The terms “insured,” “insured party,” “policyholder,” “customer,”“claimant,” and “potential claimant” are used interchangeably herein torefer to a person, party, or entity (e.g., a business or otherorganizational entity) that is covered by the insurance policy, e.g.,whose insured article or entity (e.g., property, life, health, auto,home, business) is covered by the policy. A “guarantor,” as used herein,generally refers to a person, party or entity that is responsible forpayment of the insurance premiums. The guarantor may or may not be thesame party as the insured, such as in situations when a guarantor haspower of attorney for the insured. An “annuitant,” as referred toherein, generally refers to a person, party or entity that is entitledto receive benefits from an annuity insurance product offered by theinsuring party. The annuitant may or may not be the same party as theguarantor.

Typically, a person or customer (or an agent of the person or customer)of an insurance provider fills out an application for an insurancepolicy. In some cases, the data for an application may be automaticallydetermined or already associated with a potential customer. Theapplication may undergo underwriting to assess the eligibility of theparty and/or desired insured article or entity to be covered by theinsurance policy, and, in some cases, to determine any specific terms orconditions that are to be associated with the insurance policy, e.g.,amount of the premium, riders or exclusions, waivers, and the like. Uponapproval by underwriting, acceptance of the applicant to the terms orconditions, and payment of the initial premium, the insurance policy maybe in-force, (i.e., the policyholder is enrolled).

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still cooperate or interact witheach other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also may include the plural unless itis obvious that it is meant otherwise.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

This detailed description is to be construed as examples and does notdescribe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs forevaluating properties, through the principles disclosed herein.Therefore, while particular embodiments and applications have beenillustrated and described, it is to be understood that the disclosedembodiments are not limited to the precise construction and componentsdisclosed herein. Various modifications, changes and variations, whichwill be apparent to those skilled in the art, may be made in thearrangement, operation and details of the method and apparatus disclosedherein without departing from the spirit and scope defined in theappended claims.

What is claimed:
 1. A computer-implemented method for evaluating and gamifying maintenance for a property by a user, the computer-implemented method comprising: retrieving, by one or more processors, at least one of home data for a property or user data for a user; determining, by the one or more processors and using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; receiving, by the one or more processors and from the user, a home modification indication; modifying, by the one or more processors and based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and generating, by the one or more processors and based upon the one or more modified home score factors, a home score for the property.
 2. The computer-implemented method of claim 1, further comprising: determining, based upon the user data, previous home characteristic data; wherein the one or more home score factors includes one or more difference factors different between the previous home characteristic data and the home data.
 3. The computer-implemented method of claim 2, further comprising: generating one or more recommended actions for the user to perform based upon the one or more difference factors; wherein the one or more recommended actions are home modification indications.
 4. The computer-implemented method of claim 1, wherein at least some of the home data is retrieved from one or more smart devices on the property and the home data includes at least one of: location data, environment data, first responder data, home structure data, and adherence to local construction codes.
 5. The computer-implemented method of claim 1, wherein determining the one or more home score factors includes weighting the home data and the user data, the computer-implemented method further comprising: determining influential home characteristic factors, wherein the influential home characteristic factors are a subset of the home characteristic data with the highest weight; and displaying the influential home characteristic factors to the user.
 6. The computer-implemented method of claim 1, wherein the home modification indication includes at least one of: completion of a home maintenance learning module, performance of maintenance on a component of the property, an average power consumption for the property, an average water consumption for the property, and an indication of average occupancy.
 7. The computer-implemented method of claim 1, wherein generating the home score includes: anonymizing the home score such that anonymized underwriting can be performed using the anonymized home score.
 8. A computing device for evaluating and gamifying maintenance for a property by a user, the computing device comprising: one or more processors; a communication unit; and a non-transitory computer-readable medium coupled to the one or more processors and the communication unit and storing instructions thereon that, when executed by the one or more processors, cause the computing device to: retrieve home data for a property and user data for a user; determine, using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; receive, from the user, a home modification indication; modify, based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and generate, based upon the one or more modified home score factors, a home score for the property.
 9. The computing device of claim 8, wherein the non-transitory computer-readable medium further stores instructions that, when executed by the one or more processors, cause the computing device to: determine, based upon the user data, previous home characteristic data; wherein the one or more home score factors includes one or more difference factors different between the previous home characteristic data and the home data.
 10. The computing device of claim 9, wherein the non-transitory computer-readable medium further stores instructions that, when executed by the one or more processors, cause the computing device to: generate one or more recommended actions for the user to perform based upon the one or more difference factors; wherein the one or more recommended actions are home modification indications.
 11. The computing device of claim 8, wherein at least some of the home data is retrieved from one or more smart devices on the property and the home data includes at least one of: location data, environment data, first responder data, home structure data, and adherence to local construction codes.
 12. The computing device of claim 8, wherein determining the one or more home score factors includes weighting the home data and the user data, and wherein the non-transitory computer-readable medium further stores instructions that, when executed by the one or more processors, cause the computing device to: determine influential home characteristic factors, wherein the influential home characteristic factors are a subset of the home characteristic data with the highest weight; and display the influential home characteristic factors to the user.
 13. The computing device of claim 8, wherein the home modification indication includes at least one of: completion of a home maintenance learning module, performance of maintenance on a component of the property, an average power consumption for the property, an average water consumption for the property, and an indication of average occupancy.
 14. The computing device of claim 8, wherein generating the home score includes: anonymizing the home score such that anonymized underwriting can be performed using the anonymized home score.
 15. A computer-implemented method for evaluating a score and recommending modifications for a property by a user, the computer-implemented method comprising: retrieving, by one or more processors, home data for a property and user data for a user; determining, by the one or more processors and using a trained machine learning evaluation model, one or more home score factors based upon at least the home data; generating, by the one or more processors and based upon the one or more home score factors, a home score for the property; and generating, by the one or more processors and based upon the user data and the home score, one or more recommended home modifications, wherein the one or more recommended home modifications would, if implemented, cause a modification at least one of the one or more home score factors.
 16. The computer-implemented method of claim 15, further comprising: determining, based upon the user data, previous home characteristic data; wherein the one or more home score factors includes one or more difference factors different between the previous home characteristic data and the home data.
 17. The computer-implemented method of claim 16, wherein the one or more recommended home modifications are based upon the one or more difference factors.
 18. The computer-implemented method of claim 16, wherein the one or more difference factors include at least one of: differences in environment, differences in construction codes, differences in weather, differences in landscape, or differences in wildlife.
 19. The computer-implemented method of claim 15, wherein at least some of the home data is retrieved from one or more smart devices on the property and the home data includes at least one of: location data, environment data, first responder data, home structure data, and adherence to local construction codes.
 20. The computer-implemented method of claim 15, wherein determining the one or more home score factors includes weighting the home data and the user data, and further wherein the non-transitory computer-readable medium further stores instructions that, when executed by the one or more processors, cause the computing device to: determine influential home characteristic factors, wherein the influential home characteristic factors are a subset of the home characteristic data with the highest weight; and display the influential home characteristic factors to the user. 